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From "Imran Rashid (JIRA)" <>
Subject [jira] [Commented] (SPARK-24135) [K8s] Executors that fail to start up because of init-container errors are not retried and limit the executor pool size
Date Thu, 03 May 2018 14:41:00 GMT


Imran Rashid commented on SPARK-24135:

Honestly I don't understand the failure mode described here at all, but I can make some comparisons
to yarn's handling of executor failures at the allocator level.

In yarn, spark already has a check for the number of executor failures, and it fails the entire
application if there are too many.  Its controlled by "spark.yarn.max.executor.failures".
 The failures expire over time, controlled by "spark.yarn.executor.failuresValidityInterval",
so really long running apps are not penalized by a few errors spread out over a long period
of time.  See code in ApplicationMaster & YarnAllocator.

There is also ongoing work to have spark realize where container initialization has failed,
and then request other nodes selected instead, SPARK-16630. There is a PR under review for
that now.

>From the bug description, I do think there should be some better error handling than what
there is now so the user at least knows what is going on, but it sounds like you're all in
agreement about that already :).

> [K8s] Executors that fail to start up because of init-container errors are not retried
and limit the executor pool size
> -----------------------------------------------------------------------------------------------------------------------
>                 Key: SPARK-24135
>                 URL:
>             Project: Spark
>          Issue Type: Bug
>          Components: Kubernetes
>    Affects Versions: 2.3.0
>            Reporter: Matt Cheah
>            Priority: Major
> In KubernetesClusterSchedulerBackend, we detect if executors disconnect after having
been started or if executors hit the {{ERROR}} or {{DELETED}} states. When executors fail
in these ways, they are removed from the pending executors pool and the driver should retry
requesting these executors.
> However, the driver does not handle a different class of error: when the pod enters the
{{Init:Error}} state. This state comes up when the executor fails to launch because one of
its init-containers fails. Spark itself doesn't attach any init-containers to the executors.
However, custom web hooks can run on the cluster and attach init-containers to the executor
pods. Additionally, pod presets can specify init containers to run on these pods. Therefore
Spark should be handling the {{Init:Error}} cases regardless if Spark itself is aware of init-containers
or not.
> This class of error is particularly bad because when we hit this state, the failed executor
will never start, but it's still seen as pending by the executor allocator. The executor allocator
won't request more rounds of executors because its current batch hasn't been resolved to either
running or failed. Therefore we end up with being stuck with the number of executors that
successfully started before the faulty one failed to start, potentially creating a fake resource

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